(7g) Simulation of Selectively Permeable Novel Polymeric Membranes | AIChE

(7g) Simulation of Selectively Permeable Novel Polymeric Membranes

Authors 

Soniat, M. - Presenter, Joint Center for Artificial Photosynthesis, Lawrence Berkeley National Laboratory
My research focuses on the use of computational modeling to understand systems important in energy and materials sciences. Access to cheap, reliable energy is a grand development challenge, while simultaneously the transition to clean energy sources is a grand sustainability challenge. Often, understanding processes in energy generation requires knowledge of the physical chemistry of reactions, which occur at the atomic level on the picosecond time scale, yet also being able to track energy inputs and outputs for the total macroscopic device over the course of days or even years. Having experience with a variety of modeling methodologies that address different time and length scales (density functional theory, molecular dynamics, and stochastic kinetic algorithms) uniquely positions me to address these types of systems.

Presented here, my current project at the Joint Center for Artificial Photosynthesis (JCAP, a DOE Energy Innovation Hub supported under Award Number DE-SC0004993) is aimed at improving design and understanding of novel polymeric separations membranes. The overall goal of JCAP is to develop and demonstrate a device that can convert carbon dioxide and water into useful hydrocarbon fuels using solar energy. A key determinant of performance for an artificial photosynthesis device will be the ability to block transport of products of the carbon dioxide reduction reaction (e.g., methanol, formate) while allowing ion transport (via the bicarbonate ion). Current commercially available membranes are not designed for this purpose, and the key structural features that would enable high function for carbon dioxide reduction systems have not been identified. I am performing mesoscale simulations to develop a better understanding of key membrane characteristics. Using stochastic kinetic algorithms, I model how solutes in a complex solution move into and through polymeric materials. I work closely with experimentalists to explain experimental observations and elucidate guiding principles for design of novel membrane materials. Improvements in membrane design could have impacts in a wide range of technologies including reverse osmosis and fuel cells.

Research Interests:

My overarching vision is to improve computational modeling in order to address problems in development and sustainability. Specifically, I would (1) build on my graduate work in development of advanced force fields to expand the types of systems which can be modeled accurately with molecular dynamics, (2) extend my modeling of permeation of polymeric material to better design the controlled release of drug molecules in the body, and (3) take advantage of the long time scales that can be simulated using stochastic algorithms to understand aging of materials. Connection with experiment is and will continue to be a vital part of my research.

Teaching Interests:

As a secondary school teacher in Tanzania and then a mentor to undergraduates during my doctoral and post-doctoral work, I have found that being able to relate in-classroom knowledge to the world at large is the best way to engage students with material and have them retain the information. As such, my preferred teaching style is project-based learning, especially for upper-level classes in physical chemistry. While most class time should be spent on ensuring that students have a firm grounding in the fundamentals, they would also work on a semester-long overarching project. Also related to my interest in project-based learning, a key component of my teaching would be to involve undergraduate students in research.

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